STRUCTURAL HEALTH MONITORING 2011: CONDITION-BASED MAINTENANCE AND INTELLIGENT STRUCTURES, VOL 2
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2013年
关键词:
REINFORCEMENT;
RELIABILITY;
CORROSION;
BEAMS;
D O I:
暂无
中图分类号:
T [工业技术];
学科分类号:
08 ;
摘要:
Safety and usability of existing concrete structures actually depend on the level of degradation and obsolescence due to ageing. Performance-based approaches to infrastructure management have been recently defined in terms of life-cycle-cost optimization. Lifetime functions for concrete structures can be constructed from theoretical/experimental models and, if a Structural Health Monitoring system is installed on the structure, at any required time, lifetime functions can be updated by employing a damage identification process to the data obtained from the SHM system. With reference to a real case of permanent static monitoring systems, the paper is aimed at discussing the influence of the uncertainties affecting the damage identification process on the lifetime function representing the resistance versus time of reinforced concrete beams. Based on real data, the performance estimate of the damage detection algorithms through the Receiver Operating Characteristic (ROC) curve is discussed and the probabilities of damage detection and false alarms are also formulated in terms of a Bayesian model.
机构:
La Rochelle Univ, Lab Engn Sci Environm LaSIE, CNRS, UMR 7356, La Rochelle, FranceLa Rochelle Univ, Lab Engn Sci Environm LaSIE, CNRS, UMR 7356, La Rochelle, France
Bastidas-Arteaga, E.
Rianna, G.
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机构:
Ctr Euromediterraneo Cambiamenti Climat, REg Model & Geohydrol Impacts, Lecce, ItalyLa Rochelle Univ, Lab Engn Sci Environm LaSIE, CNRS, UMR 7356, La Rochelle, France
Rianna, G.
Gervasio, H.
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机构:
Univ Coimbra, Dept Engn Civil, ISISE, Coimbra, Portugal
Univ Coimbra, Civil Engn Dept, Rua Luis Reis Santos,Polo 2, P-3030788 Coimbra, PortugalLa Rochelle Univ, Lab Engn Sci Environm LaSIE, CNRS, UMR 7356, La Rochelle, France
Gervasio, H.
Nogal, M.
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机构:
Delft Univ Technol, Fac Civil Engn & Geosci, Delft, NetherlandsLa Rochelle Univ, Lab Engn Sci Environm LaSIE, CNRS, UMR 7356, La Rochelle, France